Artificial neural networks may be used to analyze operation in a system based on known values of the system. For example, a user may be interested in analyzing sensor data, such as sensor data from semi-conductor processing equipment. A Radial Basis Function (RBF) network is an artificial neural network that uses radial basis functions as activation functions. In a typical RBF network, an RBF node or neuron is responsible for determining the activation value of the node, where each node has multiple inputs and one output. RBF networks typically can only differentiate between normal and abnormal values. Errors in RBF network analysis tend to be false negatives, and erroneous results tend to increase with increasing numbers of dimensions (e.g., numbers of sensors).